Objective
To identify the feasibility of using a deep convolutional neural network (DCNN) for the detection and localization of hip fractures on plain frontal pelvic radiographs (PXRs).
Summary of ...background data
Hip fracture is a leading worldwide health problem for the elderly. A missed diagnosis of hip fracture on radiography leads to a dismal prognosis. The application of a DCNN to PXRs can potentially improve the accuracy and efficiency of hip fracture diagnosis.
Methods
A DCNN was pretrained using 25,505 limb radiographs between January 2012 and December 2017. It was retrained using 3605 PXRs between August 2008 and December 2016. The accuracy, sensitivity, false-negative rate, and area under the receiver operating characteristic curve (AUC) were evaluated on 100 independent PXRs acquired during 2017. The authors also used the visualization algorithm gradient-weighted class activation mapping (Grad-CAM) to confirm the validity of the model.
Results
The algorithm achieved an accuracy of 91%, a sensitivity of 98%, a false-negative rate of 2%, and an AUC of 0.98 for identifying hip fractures. The visualization algorithm showed an accuracy of 95.9% for lesion identification.
Conclusions
A DCNN not only detected hip fractures on PXRs with a low false-negative rate but also had high accuracy for localizing fracture lesions. The DCNN might be an efficient and economical model to help clinicians make a diagnosis without interrupting the current clinical pathway.
Key Points
•
Automated detection of hip fractures on frontal pelvic radiographs may facilitate emergent screening and evaluation efforts for primary physicians.
• Good visualization of the fracture site by Grad-CAM enables the rapid integration of this tool into the current medical system.
• The feasibility and efficiency of utilizing a deep neural network have been confirmed for the screening of hip fractures
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Microbiota plays an important role in regulating immune responses associated with atopic diseases. We sought to evaluate relationships among airway microbiota, serum IgE levels, allergic ...sensitization and their relevance to rhinitis and asthma. Microbial characterization was performed using Illumina-based 16S rRNA gene sequencing of 87 throat swabs collected from children with asthma (n = 32) and rhinitis (n = 23), and from healthy controls (n = 32). Data analysis was performed using QIIME (Quantitative Insights Into Microbial Ecology) v1.8. Significantly higher abundance of Proteobacteria was found in children with rhinitis than in the healthy controls (20.1% vs. 16.1%, P = 0.009). Bacterial species richness (Chao1 index) and diversity (Shannon index) were significantly reduced in children with mite sensitization but not in those with food or IgE sensitization. Compared with healthy children without mite sensitization, the mite-sensitized children with rhinitis and asthma showed significantly lower Chao1 and Shannon indices. Moraxella and Leptotrichia species were significantly found in the interaction of mite sensitization with rhinitis and asthma respectively. Airway microbial diversity appears to be inversely associated with sensitization to house dust mites. A modulation between airway dysbiosis and responses to allergens may potentially cause susceptibility to rhinitis and asthma in early childhood.
Abstract
An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we ...presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics’ sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the ‘Cancer’ and ‘Gene’ sections. The ‘Survival’ panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, ‘Survival Analysis’ in ‘Customized-analysis,’ allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics’ sophisticated information, and also constructed a Summary panel in the ‘Cancer’ and ‘Gene’ sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.
Fusion transcripts are formed by either fusion genes (DNA level) or trans-splicing events (RNA level). They have been recognized as a promising tool for diagnosing, subtyping and treating cancers. ...RNA-seq has become a precise and efficient standard for genome-wide screening of such aberration events. Many fusion transcript detection algorithms have been developed for paired-end RNA-seq data but their performance has not been comprehensively evaluated to guide practitioners. In this paper, we evaluated 15 popular algorithms by their precision and recall trade-off, accuracy of supporting reads and computational cost. We further combine top-performing methods for improved ensemble detection.
Fifteen fusion transcript detection tools were compared using three synthetic data sets under different coverage, read length, insert size and background noise, and three real data sets with selected experimental validations. No single method dominantly performed the best but SOAPfuse generally performed well, followed by FusionCatcher and JAFFA. We further demonstrated the potential of a meta-caller algorithm by combining top performing methods to re-prioritize candidate fusion transcripts with high confidence that can be followed by experimental validation.
Our result provides insightful recommendations when applying individual tool or combining top performers to identify fusion transcript candidates.
With rapid industrial developments, air pollution has become a hot issue globally. Accurate prediction of PM2.5 (a category of particulate pollutant with a diameter of less than 2.5μm) has been a ...critical topic, as it can provide valuable information for government decision-making and policy control in environmental management affairs. In this paper, we propose a deep learning model based on graph neural networks (GNNs) to predict the next 48hr PM2.5 concentration in Taiwan. In this model, monitoring stations are regarded as nodes and edges are the distances between monitoring stations. Hence, the distribution of the stations can be perceived as a graph. GNNs are promising in processing non-grid structure data that can be represented as a graph. By incorporating the GNN and gated recurrent units (GRUs), this model can effectively capture the long-term spatial–temporal features in air quality time-series data. In addition, we also investigated the problem of predicting PM2.5 concentrations in the areas without monitoring stations or at sites far away from the stations. This problem has not captured researchers’ attention whose methods are based on GNN. The problem is, however, quite challenging as these areas do not have historical air quality data, leading to low prediction quality. Finally, we performed experiments to verify the effectiveness of the proposed model based on actual data sources obtained in Taiwan. The results show that the proposed model exhibits satisfactory prediction performance compared to existing models.
Abstract
This study proposes a stack framework of light gradient boosting machine (LGBM) for Taiwan stock market index prediction. Stock market predictions have been regarded as a challenging task, ...as the market is affected by several factors such as political events, general economic conditions, institutional investors' choices, movement of the global market, psychology of investors. We construct a rich feature set to capture the impacts of global markets, institutional investors' choices, and the psychology of investors. A feature selection algorithm is proposed to choose important feature subset and enhance the training performance. To further improve the prediction accuracy, we employ stacking strategy to combine multiple classifiers together. A 10‐year period of the Taiwan stock exchange capitalization weighted stock index (TAIEX) is used to verify the performance of the proposed model. The experimental results suggest that our prediction model as well as the feature selection method can achieve good prediction performance.
Features that have good predictive power for classes or output variables are useful features and hence most feature selection methods try to find them. However, since there may be high correlation or ...nonlinear dependence between such good features, we may obtain a comparable performance even when we use only a few of those good features. Thus, a feature selection method should select useful features with controlled redundancy. In this paper, we propose a novel learning method that imposes a penalty on the use of dependent/correlated features during system identification along with feature selection. This feature selection scheme can choose good features, discard indifferent, and derogatory features, and can control the level of redundancy in the set of selected features. This is probably the first attempt to feature selection with redundancy control using a fuzzy rule based framework. We have demonstrated the effectiveness of this method by utilizing a tenfold cross-validation setup on a synthetic dataset as well as on several commonly used datasets for classification problems. We have also compared our results with some state-of-the-art methods.
We previously presented the YM500 database, which contains >8000 small RNA sequencing (smRNA-seq) data sets and integrated analysis results for various cancer miRNome studies. In the updated YM500v3 ...database (http://ngs.ym.edu.tw/ym500/) presented herein, we not only focus on miRNAs but also on other functional small non-coding RNAs (sncRNAs), such as PIWI-interacting RNAs (piRNAs), tRNA-derived fragments (tRFs), small nuclear RNAs (snRNAs) and small nucleolar RNAs (snoRNAs). There is growing knowledge of the role of sncRNAs in gene regulation and tumorigenesis. We have also incorporated >10 000 cancer-related RNA-seq and >3000 more smRNA-seq data sets into the YM500v3 database. Furthermore, there are two main new sections, 'Survival' and 'Cancer', in this updated version. The 'Survival' section provides the survival analysis results in all cancer types or in a user-defined group of samples for a specific sncRNA. The 'Cancer' section provides the results of differential expression analyses, miRNA-gene interactions and cancer miRNA-related pathways. In the 'Expression' section, sncRNA expression profiles across cancer and sample types are newly provided. Cancer-related sncRNAs hold potential for both biotech applications and basic research.
In our view, the most important characteristic of a fuzzy rule-based system is its readability, which is seriously affected by, among other things, the number of features used to design the rule ...base. Hence, for high-dimensional data, dimensionality reduction through feature selection (not extraction) is very important. Our objective, here, is not to find an optimal rule base for classification but to select a set of useful features that may solve the classification problem. For this, we present an integrated mechanism for simultaneous extraction of fuzzy rules and selection of useful features. Since the feature selection method is integrated into the rule base formation, our scheme can account for possible subtle nonlinear interaction between features, as well as that between features and the tool, and, consequently, can select a set of useful features for the classification job. We have tried our method on several commonly used datasets as well as on a synthetic dataset with dimension varying from 4 to 60. Using a ten-fold cross-validation setup, we have demonstrated the effectiveness of our method.
Mesangial cells play an important role in the glomerulus to provide mechanical support and maintaine efficient ultrafiltration of renal plasma. Loss of mesangial cells due to pathologic conditions ...may lead to impaired renal function. Mesenchymal stem cells (MSC) can differentiate into many cell types, including mesangial cells. However transcriptomic profiling during MSC differentiation into mesangial cells had not been studied yet. The aim of this study is to examine the pattern of transcriptomic changes during MSC differentiation into mesangial cells, to understand the involvement of transcription factor (TF) along the differentiation process, and finally to elucidate the relationship among TF-TF and TF-key gene or biomarkers during the differentiation of MSC into mesangial cells.
Several ascending and descending monotonic key genes were identified by Monotonic Feature Selector. The identified descending monotonic key genes are related to stemness or regulation of cell cycle while ascending monotonic key genes are associated with the functions of mesangial cells. The TFs were arranged in a co-expression network in order of time by Time-Ordered Gene Co-expression Network (TO-GCN) analysis. TO-GCN analysis can classify the differentiation process into three stages: differentiation preparation, differentiation initiation and maturation. Furthermore, it can also explore TF-TF-key genes regulatory relationships in the muscle contraction process.
A systematic analysis for transcriptomic profiling of MSC differentiation into mesangial cells has been established. Key genes or biomarkers, TFs and pathways involved in differentiation of MSC-mesangial cells have been identified and the related biological implications have been discussed. Finally, we further elucidated for the first time the three main stages of mesangial cell differentiation, and the regulatory relationships between TF-TF-key genes involved in the muscle contraction process. Through this study, we have increased fundamental understanding of the gene transcripts during the differentiation of MSC into mesangial cells.
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